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Symmetry
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Advances in Medical Image Segmentation
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Advances in Medical Image Segmentation

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 January 2018) | Viewed by 47038

Special Issue Editor

Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, there is an increasing interest in computer-aided medical image processing, in a way to develop more accurate medical diagnosis systems. Medical image segmentation constitutes a fundamental processing task that needs to be applied initially in order to extract specific regions of interest of any modality medical image. Due to the rapid development of powerful medical imaging devices, able to provide high resolution images of multiple volumes, the needs for efficient (accuracy, speed) segmentation methodologies have emerged. A good segmentation result can be very useful for any medical diagnosis system and the doctors as well, since it can help towards the diagnosis of a disease in an early stage and thus the application of more effective treatments. In the light of these needs, this special issue manages to get a snapshot of the current advances in segmentation of medical images of any modality.

Τhis Special Issue aims to publish high-quality research papers, as well as review articles addressing emerging trends in medical image segmentation. Original contributions, not currently under review to a journal or a conference, are solicited in relevant areas including, but not limited to, the following:

  • Machine learning
  • Deep learning
  • Kernel methods
  • Shape modeling
  • Soft computing methods
  • Knowledge based segmentation
  • High performance computing implementations (e.g. GPU, GRID, CLOUD)
  • Review/Surveys of segmentation methods
  • New image datasets
  • Different image modalities (e.g. CT, MRI, PET)
  • Evaluation metrics

Prof. Dr. George A. Papakostas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

16 pages, 4144 KiB  
Article
Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis
by Baidaa Al-Bander, Bryan M. Williams, Waleed Al-Nuaimy, Majid A. Al-Taee, Harry Pratt and Yalin Zheng
Symmetry 2018, 10(4), 87; https://doi.org/10.3390/sym10040087 - 30 Mar 2018
Cited by 144 | Viewed by 19704
Abstract
Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) [...] Read more.
Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR); however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC). The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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25 pages, 9487 KiB  
Article
Intelligent Image Processing System for Detection and Segmentation of Regions of Interest in Retinal Images
by Dan Popescu and Loretta Ichim
Symmetry 2018, 10(3), 73; https://doi.org/10.3390/sym10030073 - 17 Mar 2018
Cited by 7 | Viewed by 5220
Abstract
The automatic detection, segmentation, localization, and evaluation of the optic disc, macula, exudates, and hemorrhages are very important for diagnosing retinal diseases. One of the difficulties in detecting such regions of interest (RoIs) with computer vision is their symmetries, e.g., between the optic [...] Read more.
The automatic detection, segmentation, localization, and evaluation of the optic disc, macula, exudates, and hemorrhages are very important for diagnosing retinal diseases. One of the difficulties in detecting such regions of interest (RoIs) with computer vision is their symmetries, e.g., between the optic disc and exudates and also between exudates and hemorrhages. This paper proposes an original, intelligent, and high-performing image processing system for the simultaneous detection and segmentation of retinal RoIs. The basic principles of the method are image decomposition in small boxes and local texture analysis. The processing flow contains three phases: preprocessing, learning, and operating. As a first novelty, we propose proper feature selection based on statistical analysis in confusion matrices for different feature types (extracted from a co-occurrence matrix, fractal type, and local binary patterns). Mainly, the selected features are chosen to differentiate between similar RoIs. The second novelty consists of local classifier fusion. To this end, the local classifiers associated with features are grouped in global classifiers corresponding to the RoIs. The local classifiers are based on minimum distances to the representatives of classes and the global classifiers are based on confidence intervals, weights, and a voting scheme. A deep convolutional neural network, based on supervised learning, for blood vessel segmentation is proposed in order to improve the RoI detection performance. Finally, the experimental results on real images from different databases demonstrate the rightness of our methodologies and algorithms. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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16 pages, 4628 KiB  
Article
Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images
by Karolina Nurzynska
Symmetry 2018, 10(3), 60; https://doi.org/10.3390/sym10030060 - 06 Mar 2018
Cited by 28 | Viewed by 4625
Abstract
The automatic analysis of the state of the corneal endothelium is of much interest in ophthalmology. Up till now, several manual and semi-automatic methods have been introduced, but the need of fully-automatic segmentation of cells in the endothelium is still in search. This [...] Read more.
The automatic analysis of the state of the corneal endothelium is of much interest in ophthalmology. Up till now, several manual and semi-automatic methods have been introduced, but the need of fully-automatic segmentation of cells in the endothelium is still in search. This work addresses the problem of automatic delineation of cells in the corneal endothelium images and suggests to use the convolutional neural network (CNN) to classify between cell center, cell body, and cell border in order to achieve precise segmentation. Additionally, a method to automatically select and split merged cells is given. In order to skeletonize the result, the best-fit method is used. The achieved outcomes are compared to manual annotations in order to define the mutual overlapping. The Dice index, Jaccard coefficient, modified Hausdorff distance, and several other metrics for mosaic overlapping are used. As a final check-up, the visual inspection is shown. The performed experiments revealed the best architecture for CNN. The correctness and precision of the segmentation were evaluated on Endothelial Cell “Alizarine” dataset. According to the Dice index and Jaccard coefficient, the automatically achieved cell delineation overlaps the original one with 93% precision. While modified Hausdorff distance shows 0.14 pixel distance, proving very high accuracy. These findings are confirmed by other metrics and also supported by presented visual inspection of achieved segmentations. To conclude, the methodology to achieve fully-automatic delineation of cell boundaries in the corneal endothelium images was presented. The segmentation obtained as a result of pixel classification with CNN proved very high precision. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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21 pages, 3737 KiB  
Article
Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images
by Nilanjan Dey, Venkatesan Rajinikanth, Amira S. Ashour and João Manuel R. S. Tavares
Symmetry 2018, 10(2), 51; https://doi.org/10.3390/sym10020051 - 22 Feb 2018
Cited by 127 | Viewed by 6165
Abstract
The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the [...] Read more.
The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard’s coefficient, Dice’s coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur’s thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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13 pages, 4399 KiB  
Article
Three-Dimensional Finite Element Analysis of Maxillary Sinus Floor Augmentation with Optimal Positioning of a Bone Graft Block
by Peter Schuller-Götzburg, Thomas Forte, Werner Pomwenger, Alexander Petutschnigg, Franz Watzinger and Karl Entacher
Symmetry 2018, 10(2), 33; https://doi.org/10.3390/sym10020033 - 23 Jan 2018
Cited by 6 | Viewed by 5524
Abstract
Purpose: the aim of the computational 3D-finite element study is to evaluate the influence of an augmented sinus lift with additional inserted bone grafting. The bone graft block stabilizes the implant in conjunction with conventional bone augmentation. Two finite element models were applied: [...] Read more.
Purpose: the aim of the computational 3D-finite element study is to evaluate the influence of an augmented sinus lift with additional inserted bone grafting. The bone graft block stabilizes the implant in conjunction with conventional bone augmentation. Two finite element models were applied: the real geometry based bone models and the simplified geometry models. The bone graft block was placed in three different positions. The implants were loaded first with an axial force and then with forces simulating laterotrusion and protrusion. This study examines whether the calculated stress behavior is symmetrical for both models. Having established a symmetry between the primary axis, the laterotrusion and protrusion behavior reduces calculation efforts, by simplifying the model. Material and Methods: a simplified U-shaped 3D finite element model of the molar region of the upper jaw and a more complex anatomical model of the left maxilla with less cortical bone were created. The bone graft block was placed in the maxillary sinus. Then the von Mises stress distribution was calculated and analyzed at three block positions: at contact with the sinus floor, in the middle of the implant helix and in the upper third of the implant. The two finite element models were then compared to simplify the modelling. Results: the position of the bone graft block significantly influences the magnitude of stress distribution. A bone graft block positioned in the upper third or middle of the implant reduces the quantity of stress compared to the reference model without a bone graft block. The low bone graft block position is clearly associated with lower stress distribution in compact bone. We registered no significant differences in stress in compact bone with regard to laterotrusion or protrusion. Conclusions: maximum values of von Mises stresses in compact bone can be reduced significantly by using a bone graft block. The reduction of stress is nearly the same for positions in the upper third and the middle of the implant. It is much more pronounced when the bone graft block is in the lower third of the implant near the sinus floor, which appeared to be the best position in the present study. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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12264 KiB  
Article
Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods
by Marcin Ciecholewski
Symmetry 2017, 9(11), 277; https://doi.org/10.3390/sym9110277 - 16 Nov 2017
Cited by 22 | Viewed by 5012
Abstract
The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were [...] Read more.
The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to segment the masses: an edge–based active contour model using an inflation/deflation force with a damping coefficient (EM), a geometric active contour model (GAC) and an active contour without edges (ACWE). The preprocessing techniques presented in this publication are to reduce noise and at the same time amplify uniform areas of images in order to improve segmentation results. In addition, the use of image sampling by bicubic interpolation was tested to shorten the evolution time of active contour methods. The experiments used a test set composed of 100 cases taken from two publicly available databases: Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) database. The qualitative assessment concerned the ability to formulate an adequate diagnostic hypothesis and, for the individual methods (malignant and benign cases together), it amounted to at least: 81% (EM), 76% (GAC), and 69% (ACWE). The quantitative test consisted of measuring the following indexes: overlap value (OV) and extra fraction (EF). The OV of the segmentation for malignant and benign cases had the following average values: 0.81 ∓ 0.10 (EM), 0.79 ∓ 0.09 (GAC), 0.76 ∓ 0.18 (ACWE). The average values of the EF index, in turn, amounted to: 0.07 ∓ 0.06 (EM), 0.07 ∓ 0.05 (GAC) 0.34 ∓ 0.32 (ACWE). The qualitative and quantitative results obtained are the best for EM and are comparable or better than for other methods presented in the literature. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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